What are Open Source AI Models for Multimodal Tasks?
Open source AI models for multimodal tasks are advanced vision-language models (VLMs) that can simultaneously process and understand multiple types of input—including text, images, videos, and documents. These sophisticated models combine natural language processing with computer vision to perform complex reasoning, analysis, and generation across different modalities. They enable applications ranging from document understanding and visual question answering to 3D spatial reasoning and interactive AI agents, democratizing access to state-of-the-art multimodal AI capabilities for researchers, developers, and enterprises worldwide.
GLM-4.5V
GLM-4.5V is the latest generation vision-language model released by Zhipu AI, built upon the flagship GLM-4.5-Air with 106B total parameters and 12B active parameters. Utilizing a Mixture-of-Experts (MoE) architecture, it achieves superior performance at lower inference cost. The model introduces 3D Rotated Positional Encoding (3D-RoPE) for enhanced 3D spatial reasoning and features a 'Thinking Mode' switch for balancing quick responses with deep reasoning across images, videos, and long documents.
GLM-4.5V: State-of-the-Art Multimodal Reasoning
GLM-4.5V represents the pinnacle of open source multimodal AI, featuring 106B total parameters with 12B active parameters through an innovative MoE architecture. This latest generation VLM excels in processing diverse visual content including images, videos, and long documents, achieving state-of-the-art performance on 41 public multimodal benchmarks. Its groundbreaking 3D-RoPE technology significantly enhances perception and reasoning for 3D spatial relationships, while the flexible 'Thinking Mode' allows users to optimize between speed and analytical depth.
Pros
- State-of-the-art performance on 41 multimodal benchmarks.
- Innovative 3D-RoPE for superior 3D spatial reasoning.
- MoE architecture provides excellent efficiency at scale.
Cons
- Higher computational requirements due to 106B parameters.
- More complex deployment compared to smaller models.
Why We Love It
- It sets new standards in multimodal AI with breakthrough 3D spatial reasoning and flexible thinking modes for diverse applications.
GLM-4.1V-9B-Thinking
GLM-4.1V-9B-Thinking is an open-source Vision-Language Model jointly released by Zhipu AI and Tsinghua University's KEG lab. Built on GLM-4-9B-0414, it introduces a 'thinking paradigm' with Reinforcement Learning with Curriculum Sampling (RLCS). Despite being only 9B parameters, it achieves performance comparable to much larger 72B models, excelling in STEM problem-solving, video understanding, and long document analysis with support for 4K image resolution.
GLM-4.1V-9B-Thinking: Compact Powerhouse for Complex Reasoning
GLM-4.1V-9B-Thinking demonstrates that parameter efficiency doesn't compromise performance. This 9B-parameter model rivals much larger alternatives through its innovative 'thinking paradigm' and RLCS training methodology. It excels across diverse multimodal tasks including STEM problem-solving, video understanding, and long document comprehension, while supporting high-resolution 4K images with arbitrary aspect ratios. The model represents a breakthrough in achieving state-of-the-art multimodal reasoning at a fraction of the computational cost.
Pros
- Exceptional performance rivaling 72B parameter models.
- Innovative 'thinking paradigm' enhances reasoning capabilities.
- Supports 4K image resolution with arbitrary aspect ratios.
Cons
- Smaller model size may limit some complex reasoning tasks.
- Less context length compared to larger alternatives.
Why We Love It
- It proves that smart architecture and training can deliver world-class multimodal performance in a compact, efficient package perfect for resource-conscious deployments.
Qwen2.5-VL-32B-Instruct
Qwen2.5-VL-32B-Instruct is a multimodal large language model from the Qwen team, excelling in analyzing texts, charts, icons, graphics, and layouts within images. It functions as a visual agent capable of reasoning and tool direction, supporting computer and phone use. The model accurately localizes objects and generates structured outputs for data like invoices and tables, with enhanced mathematical abilities through reinforcement learning and human preference alignment.
Qwen2.5-VL-32B-Instruct: Versatile Visual Agent
Qwen2.5-VL-32B-Instruct stands out as a comprehensive multimodal solution designed for practical applications. Beyond standard object recognition, it excels in document analysis, chart interpretation, and structured data extraction from complex visual content. Its visual agent capabilities enable dynamic tool usage and interactive computing tasks, while enhanced mathematical reasoning through reinforcement learning makes it ideal for analytical workflows. With 131K context length and human-aligned responses, it bridges the gap between AI capability and real-world usability.
Pros
- Excellent document analysis and structured data extraction.
- Visual agent capabilities for interactive computing tasks.
- 131K context length for processing long documents.
Cons
- Mid-range parameter count may limit some specialized tasks.
- Higher pricing compared to smaller efficient models.
Why We Love It
- It excels as a practical visual agent that seamlessly handles document analysis, structured data extraction, and interactive computing tasks with human-aligned responses.
Multimodal AI Model Comparison
In this comprehensive comparison, we analyze 2025's leading open source multimodal AI models, each optimized for different aspects of vision-language tasks. GLM-4.5V offers state-of-the-art performance with innovative 3D reasoning, GLM-4.1V-9B-Thinking provides exceptional efficiency without sacrificing capability, and Qwen2.5-VL-32B-Instruct excels in practical applications and document analysis. This side-by-side comparison helps you select the optimal model for your specific multimodal AI requirements.
| Number | Model | Developer | Subtype | Pricing (SiliconFlow) | Core Strength |
|---|---|---|---|---|---|
| 1 | GLM-4.5V | Zhipu AI | Vision-Language Model | $0.14-$0.86/M Tokens | 3D spatial reasoning & thinking modes |
| 2 | GLM-4.1V-9B-Thinking | THUDM | Vision-Language Model | $0.035-$0.14/M Tokens | Efficient performance matching 72B models |
| 3 | Qwen2.5-VL-32B-Instruct | Qwen Team | Vision-Language Model | $0.27/M Tokens | Visual agent & document analysis |
Frequently Asked Questions
Our top three picks for 2025 are GLM-4.5V, GLM-4.1V-9B-Thinking, and Qwen2.5-VL-32B-Instruct. Each model excels in different aspects of multimodal AI: GLM-4.5V for state-of-the-art performance and 3D reasoning, GLM-4.1V-9B-Thinking for efficiency and compact excellence, and Qwen2.5-VL-32B-Instruct for practical visual agent capabilities.
For cutting-edge research and 3D spatial tasks, GLM-4.5V is optimal. For resource-efficient deployments requiring strong reasoning, GLM-4.1V-9B-Thinking is ideal. For business applications involving document analysis, chart interpretation, and structured data extraction, Qwen2.5-VL-32B-Instruct provides the best practical performance.